SoroushVahidi/scidocs huggingface dataset:
Root coverage in molar teeth: a comparative controlled randomized clinical trial.
(2012)

AIM
To compare the clinical outcomes of laterally moved, coronally advanced flap (LMCAF) versus Bilaminar technique (BT) in the treatment of single gingival recession on molar teeth.


MATERIAL AND METHODS
Fifty patients showing Miller I and II gingival recessions at first molar teeth were treated: 25 were randomly assigned to the BT group and 25 belonged to the LMCAF group. Patient's post-operative morbidity was assessed 1 week after the surgery, while aesthetic evaluation and the clinical evaluation were made 1 year later.


RESULTS
No statistically significant difference was demonstrated in terms of recession and PPD reduction. Statistically greater probability of complete root coverage (CRC, Odds Ratio 22.1) and greater increase in gingival thickness were observed in the BT group. Greater increase in keratinized tissue was obtained in the LMCAF. Patient satisfaction with aesthetics was very high in both treatment groups. Better post-operative course was observed in the LMCAF, while better post-operative sensitivity and root coverage evaluation were demonstrated in patients treated with BT.


CONCLUSIONS
Gingival recession at first molar teeth can be successfully treated with LMCAF and BT. Better CRC was achieved with BT, while more comfortable post-operative course was associated with the LMCAF.
Ontology-based text summarization. The case of Texminer
(2014)

Purpose – The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems. Design/methodology/approach – The paper analyzes the main literature in this field and presents the structure and features of Texminer, a software that facilitates summarization of texts on Port and Coastal Engineering. Texminer entails a combination of several techniques, including: socio-cognitive user models, Natural Language Processing, disambiguation and ontologies. After processing a corpus, the system was evaluated using as a reference various clustering evaluation experiments conducted by Arco (2008) and Hennig et al. (2008). The results were checked with a support vector machine, Rouge metrics, the F-measure and calculation of precision and recall. Findings – The experiment illustrates the superiority of abstracts obtained through the assistance of ontology-based techniques. Originality/value – The authors were able to corroborate that the summaries obtained using Texminer are more efficient than those derived through other systems whose summarization models do not use ontologies to summarize texts. Thanks to ontologies, main sentences can be selected with a broad rhetorical structure, especially for a specific knowledge domain.
Model-based Security Testing: An Empirical Study on OAuth 2.0 Implementations
(2016)

Motivated by the prevalence of OAuth-related vulnerabilities in the wild, large-scale security testing of real-world OAuth 2.0 implementations have received increasing attention lately [31,37,42]. However, these existing works either rely on manual discovery of new vulnerabilities in OAuth 2.0 implementations or perform automated testing for specific, previously-known vulnerabilities across a large number of OAuth implementations. In this work, we propose an adaptive model-based testing framework to perform automated, large-scale security assessments for OAuth 2.0 implementations in practice. Key advantages of our approach include (1) its ability to identify existing vulnerabilities and discover new ones in an automated manner; (2) improved testing coverage as all possible execution paths within the scope of the model will be checked and (3) its ability to cater for the implementation differences of practical OAuth systems/ applications, which enables the analyst to offload the manual efforts for large-scale testing of OAuth implementations. We have designed and implemented OAuthTester to realize our proposed framework. Using OAuthTester, we examine the implementations of 4 major Identity Providers as well as 500 top-ranked US and Chinese websites which use the OAuth-based Single-Sign-On service provided by the formers. Our empirical findings demonstrate the efficacy of adaptive model-based testing on OAuth 2.0 deployments at scale. More importantly, OAuthTester not only manages to rediscover various existing vulnerabilities but also identify several previously unknown security flaws and new exploits for a large number of eal-world applications implementing OAuth 2.0.
Discovery of inference rules for question-answering
(2001)

One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as X writes Y implies X is the author of Y in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.
Efficient clustering of high-dimensional data sets with application to reference matching
(2000)

Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once—for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under reasonable assumptions about the cheap distance metric, this reduction in computational cost comes without any loss in clustering accuracy. Canopies can be applied to many domains and used with a variety of clustering approaches, including Greedy Agglomerative Clustering, K-means and Expectation-Maximization. We present experimental results on grouping bibliographic citations from the reference sections of research papers. Here the canopy approach reduces computation time over a traditional clustering approach by more than an order of magnitude and decreases error in comparison to a previously used algorithm by 25%.
Pedestrian intention recognition using Latent-dynamic Conditional Random Fields
(2015)

We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches.
Explainable User Clustering in Short Text Streams
(2016)

User clustering has been studied from different angles: behavior-based, to identify similar browsing or search patterns, and content-based, to identify shared interests. Once user clusters have been found, they can be used for recommendation and personalization. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of short text streams. User clustering in this setting is more challenging than in the case of long documents as it is difficult to capture the users' dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (or UCT for short). UCT adaptively tracks changes of each user's time-varying topic distribution based both on the short texts the user posts during a given time period and on the previously estimated distribution. To infer changes, we propose a Gibbs sampling algorithm where a set of word-pairs from each user is constructed for sampling. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed clustering model compared to state-of-the-art baselines.
Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion
(2010)

Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers’ gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.
Variable Selection via Penalized Neural Network : a Drop-Out-One Loss Approach
(2018)

We propose a variable selection method for high dimensional regression models, which allows for complex, nonlinear, and high-order interactions among variables. The proposed method approximates this complex system using a penalized neural network and selects explanatory variables by measuring their utility in explaining the variance of the response variable. This measurement is based on a novel statistic called Drop-Out-One Loss. The proposed method also allows (overlapping) group variable selection. We prove that the proposed method can select relevant variables and exclude irrelevant variables with probability one as the sample size goes to infinity, which is referred to as the Oracle Property. Experimental results on simulated and real world datasets show the efficiency of our method in terms of variable selection and prediction accuracy.
Abstraction and reformulation in artificial intelligence.
(2003)

This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.
Visual Tracking with Fully Convolutional Networks
(2015)

We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet. The discoveries motivate the design of our tracking system. It is found that convolutional layers in different levels characterize the target from different perspectives. A top layer encodes more semantic features and serves as a category detector, while a lower layer carries more discriminative information and can better separate the target from distracters with similar appearance. Both layers are jointly used with a switch mechanism during tracking. It is also found that for a tracking target, only a subset of neurons are relevant. A feature map selection method is developed to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Extensive evaluation on the widely used tracking benchmark [36] shows that the proposed tacker outperforms the state-of-the-art significantly.
Hybrid Fuzz Testing: Discovering Software Bugs via Fuzzing and Symbolic Execution
(2012)

Random mutational fuzz testing (fuzzing) and symbolic executions are program testing techniques that have been gaining popularity in the security research community. Fuzzing finds bugs in a target program by natively executing it with random inputs while monitoring the execution for abnormal behaviors such as crashes. While fuzzing may have a reputation of being able to explore deep into a program’s state space efficiently, naı̈ve fuzzers usually have limited code coverage for typical programs since unconstrained random inputs are unlikely to drive the execution down many different paths. In contrast, symbolic execution tests a program by treating the program’s input as symbols and interpreting the program over such symbolic inputs. Although in theory symbolic execution is guaranteed to be effective in achieving code coverage if we explore all possible paths, this generally requires exponential resource and is thus not practical for many real-world programs. This thesis presents our attempt to attain the best of both worlds by combining fuzzing with symbolic execution in a novel manner. Our technique, called hybrid fuzzing, first uses symbolic execution to discover frontier nodes that represent unique paths in the program. After collecting as many frontier nodes as possible under a user-specifiable resource constraint, it transits to fuzz the program with preconditioned random inputs, which are provably random inputs that respect the path predicate leading to each frontier node. Our current implementation supports programs with linear path predicates and can automatically generate preconditioned random inputs from a polytope model of the input space extracted from binaries. These preconditioned random inputs can then be used with any fuzzer. Experiments show that our implementation is efficient in both time and space, and the inputs generated by it are able to gain extra breadth and depth over previous approaches.
DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
(2014)

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.
Deep Constrained Clustering - Algorithms and Advances
(2019)

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. We explore a deep learning formulation of constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our formulation can not only handle standard together/apart constraints without the well documented negative effects reported but can also model instance level constraints (level-of-difficulty), cluster level constraints (balancing cluster size) and triplet constraints. The first two are new ways for domain experts to enforce guidance whilst the later importantly allows generating ordering constraints from continuous side-information.
MOTHERS WHO KILL THEIR CHILDREN A literature review
(2008)

Maternal filicide, the murder of a child by its mother, is a complex phenomenon with various causes and characteristics. Research, by means of the development of several classification systems and in identifying particular risk factors, has been conducted with the aim of better prevention of this emotionally evocative crime. Various disciplines have offered a wide range of perspectives on why women kill their biological children. These are intended to yield a better understanding of the aetiology of this crime. This literature review delineates three dominant perspectives: psychiatric, psychological, and sociological. The main findings of each perspective are discussed. However, these three perspectives frequently operate in conjunction with each other in that both intrapsychic and interpersonal dynamics play a role in acts of maternal filicide. The most vulnerable women appear to be those who have had a severely deficient developmental history (trauma and/or grossly inadequate parenting), those who experience current difficult psychosocial circumstances, and those who have been diagnosed with a psychiatric illness. However, not all women who experience such problems kill their children. In this regard, individual differences have an important role to play and more carefully delineated future research is suggested. One of the most significant findings of this literature review is that there appears to be a paucity of systematic research on the South African phenomenon of parental child homicide.
Fast trajectory clustering using Hashing methods
(2016)

There has been an explosion in the usage of trajectory data. Clustering is one of the simplest and most powerful approaches for knowledge discovery from trajectories. In order to produce meaningful clusters, well-defined metrics are required to capture the essence of similarity between trajectories. One such distance function is Dynamic Time Warping (DTW), which aligns two trajectories together in order to determine similarity. DTW has been widely accepted as a very good distance measure for trajectory data. However, trajectory clustering is very expensive due to the complexity of the similarity functions, for example, DTW has a high computational cost O(n2), where n is the average length of the trajectory, which makes the clustering process very expensive. In this paper, we propose the use of hashing techniques based on Distance-Based Hashing (DBH) and Locality Sensitive Hashing (LSH) to produce approximate clusters and speed up the clustering process.
Human-computer interaction for hybrid carving
(2013)

In this paper we explore human-computer interaction for carving, building upon our previous work with the FreeD digital sculpting device. We contribute a new tool design (FreeD V2), with a novel set of interaction techniques for the fabrication of static models: personalized tool paths, manual overriding, and physical merging of virtual models. We also present techniques for fabricating dynamic models, which may be altered directly or parametrically during fabrication. We demonstrate a semi-autonomous operation and evaluate the performance of the tool. We end by discussing synergistic cooperation between human and machine to ensure accuracy while preserving the expressiveness of manual practice.
ExtRA: Extracting Prominent Review Aspects from Customer Feedback
(2018)

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost everyday. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.